Impact of generative AI interaction and output quality on university students’ learning outcomes: a technology-mediated and motivation-driven approach

Abstract This study investigates the influence of generative artificial intelligence (GAI) on university students’ learning outcomes, employing a technology-mediated learning perspective. We developed and empirically tested an integrated model, grounded in interaction theory and technology-mediated learning theory, to examine the relationships between GAI interaction quality, GAI output quality, and learning outcomes. The model incorporates motivational factors (learning motivation, academic self-efficacy, and creative self-efficacy) as mediators and creative thinking as a moderator. Data from 323 Chinese university students, collected through a two-wave longitudinal survey, revealed that both GAI interaction quality and output quality positively influenced learning motivation and creative self-efficacy. Learning motivation significantly mediated the relationship between GAI output quality and learning outcomes. Furthermore, creative thinking moderated several pathways within the model, with some variations observed across the two time points. These findings provide theoretical and practical insights into the effective integration of GAI tools in higher education, highlighting the importance of both interaction and output quality in optimizing student learning experiences.

Abstract licence: CC BY-NC-ND

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